I recently listened to Pieter Levels on the Lex Friedman Podcast, and it was eye-opening. Pieter has built numerous successful micro-SaaS businesses by running his applications on single server, avoiding cloud infrastructure complexity, and focusing on what truly matters: product-market fit.
My desk setup as of writing this post. I've been working from home for the past 1 year and have been slowly evolving my setup to be more ergonomic and efficient.
Also, I want to use this blog to track changes for my setup over time and share with fellow devs / team-mates.
In the ever-evolving landscape of LLMs, I've observed two distinct camps: the doomsayers who predict a dystopian future and the overly optimistic who claim AI has completely transformed their lives overnight. As for me? I find myself somewhere in the middle – cautiously optimistic about the technology's potential while actively seeking ways to harness it for practical, everyday use.
Understanding observability in AI applications, particularly in Large Language Models (LLMs), is crucial. It's all about tracking how your model performs over time, which is especially challenging with text generation outputs. Unlike categorical outputs, text generation can vary widely, making it essential to monitor the behavior and performance of your model closely.